{"type":"ScholarlyArticle","title":"ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers","authors":[{"name":"Luoyu Mei","slug":"luoyu-mei","given":"Luoyu","family":"Mei","affiliation":"Southeast University ,; City University of Hong Kong ,"},{"name":"Shuai Wang","slug":"shuai-wang","given":"Shuai","family":"Wang","affiliation":"Southeast University ,"},{"name":"Yun Cheng","slug":"yun-cheng","given":"Yun","family":"Cheng","affiliation":"Swiss Data Science Center , Zurich , Switzerland,"},{"name":"Ruofeng Liu","slug":"ruofeng-liu","given":"Ruofeng","family":"Liu","affiliation":"Robert Bosch LLC ,"},{"name":"Zhimeng Yin","slug":"zhimeng-yin","given":"Zhimeng","family":"Yin","affiliation":"City University of Hong Kong ,"},{"name":"Wenchao Jiang","slug":"wenchao-jiang","given":"Wenchao","family":"Jiang","affiliation":"Singapore University of Technology and Design ,"},{"name":"Shuai Wang","slug":"shuai-wang","given":"Shuai","family":"Wang","affiliation":"Southeast University ,"},{"name":"Wei Gong","slug":"wei-gong","given":"Wei","family":"Gong","affiliation":"University of Science and Technology of China"}],"abstract":"Semantic recognition is pivotal in virtual reality (VR) applications, enabling immersive and interactive experiences. A promising approach is utilizing millimeter-wave (mmWave) signals to generate point clouds. However, the high computational and memory demands of current mmWave point cloud models hinder their efficiency and reliability. To address this limitation, our paper introduces ESP-PCT, a novel Enhanced Semantic Performance Point Cloud Transformer with a two-stage semantic recognition framework tailored for VR applications. ESP-PCT takes advantage of the accuracy of sensory point cloud data and optimizes the semantic recognition process, where the localization and focus stages are trained jointly in an end-to-end manner. We evaluate ESP-PCT on various VR semantic recognition conditions, demonstrating substantial enhancements in recognition efficiency. Notably, ESP-PCT achieves a remarkable accuracy of 93.2% while reducing the computational requirements (FLOPs) by 76.9% and memory usage by 78.2% compared to the existing Point Transformer model simultaneously. These underscore ESP-PCT's potential in VR semantic recognition by achieving high accuracy and reducing redundancy. The code and data of this project are available at \\url{https://github.com/lymei-SEU/ESP-PCT}.","doi":"10.24963/ijcai.2024/131","url":"https://ijcai.org/proceedings/2024/131","pdf":"https://ijcai.org/proceedings/2024/0131.pdf","markdown":"https://ijcai.org/proceedings/2024/131.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/131","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":131,"pages":{"first":1182,"last":1190},"keywords":["Computer Vision: CV: Applications","Multidisciplinary Topics and Applications: MTA: Security and privacy","Machine Learning: ML: Optimization","Computer Vision: CV: Motion and tracking"],"videos":[],"references":[{"position":1,"raw":"[Arnab et al., 2021] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision (ICCV), pages 6836-6846, October 2021.","title":"Vivit: A video vision transformer","authors":"Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Basak and Gowda, 2022] Suryoday Basak and Mahanth Gowda. mmspy: Spying phone calls using mmwave radars. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1211-1228, 2022.","title":"mmspy: Spying phone calls using mmwave radars","authors":"Gowda Basak, Suryoday Basak, Mahanth Gowda","year":"2022","venue":"IEEE Symposium on Security and Privacy (SP)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2022] Dongjiang Cao, Ruofeng Liu, Hao Li, Shuai Wang, Wenchao Jiang, and Chris Xiaoxuan Lu. Cross vision-rf gait re-identification with low-cost rgb-d cameras and mmwave radars. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technolo- gies, 6(3):1-25, 2022.","title":"Cross vision-rf gait re-identification with low-cost rgb-d cameras and mmwave radars","authors":"Dongjiang Cao, Ruofeng Liu, Hao Li, Shuai Wang, Wenchao Jiang, Chris Xiaoxuan Lu","year":"2022","venue":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2023] Shoufa Chen, Peize Sun, Yibing Song, and Ping Luo. Diffusiondet: Diffusion model for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 19830- 19843, October 2023.","title":"Diffusiondet: Diffusion model for object detection","authors":"Chen","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[da Silveira et al., 2022] Thiago L. T. da Silveira, Paulo G. L. Pinto, Jeffri Murrugarra-Llerena, and Cláudio R. Jung. 3d scene geometry estimation from 360° imagery: A survey. ACM Comput. Surv., 55(4), nov 2022.","title":"3d scene geometry estimation from 360° imagery: A survey","authors":"L T Thiago, Paulo G L Da Silveira, Jeffri Pinto, Cláudio R Murrugarra-Llerena, Jung","year":"2022","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dong et al., 2022] Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo. Cswin transformer: A general vision transformer backbone with cross-shaped windows. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12124-12134, June 2022.","title":"Cswin transformer: A general vision transformer backbone with cross-shaped windows","authors":"Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fan et al., 2021] Hehe Fan, Yi Yang, and Mohan Kankan- halli. Point 4d transformer networks for spatio-temporal modeling in point cloud videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14204-14213, June 2021.","title":"Point 4d transformer networks for spatio-temporal modeling in point cloud videos","authors":"Fan","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Feng et al., 2024] Tuo Feng, Ruijie Quan, Xiaohan Wang, Wenguan Wang, and Yi Yang. Interpretable3d: An ad- hoc interpretable classifier for 3d point clouds. Proceed- ings of the AAAI Conference on Artificial Intelligence, 38(2):1761-1769, Mar. 2024.","title":"Interpretable3d: An adhoc interpretable classifier for 3d point clouds","authors":"Tuo Feng, Ruijie Quan, Xiaohan Wang, Wenguan Wang, Yi Yang","year":"2024","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Graves et al., 2005] Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. Bidirectional lstm networks for improved phoneme classification and recognition. In Włodzisław Duch, Janusz Kacprzyk, Erkki Oja, and Sławomir Zadrożny, editors, Artificial Neural Networks: Formal Models and Their Applications -ICANN 2005, pages 799-804, Berlin, Heidelberg, 2005. Springer Berlin Heidelberg.","title":"Bidirectional lstm networks for improved phoneme classification and recognition","authors":"Alex Graves, Santiago Fernández, Jürgen Schmidhuber","year":"2005","venue":"Artificial Neural Networks: Formal Models and Their Applications -ICANN 2005","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gu et al., 2022] Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, and David Z. Pan. Multi-scale high-resolution vision transformer for semantic segmentation. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12094-12103, June 2022.","title":"Multi-scale high-resolution vision transformer for semantic segmentation","authors":"Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, David Z Pan","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Han et al., 2023] Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, and Dacheng Tao. A survey on vision transformer. IEEE Transactions on Pattern Analysis and Machine In- telligence, 45(1):87-110, 2023.","title":"A survey on vision transformer","authors":"Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Herzig et al., 2022] Roei Herzig, Elad Ben-Avraham, Kart- tikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, and Amir Globerson. Object- region video transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3148-3159, June 2022.","title":"Objectregion video transformers","authors":"Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hu et al., 2024] Youbing Hu, Yun Cheng, Anqi Lu, Zhiqiang Cao, Dawei Wei, Jie Liu, and Zhijun Li. Lf-vit: Reducing spatial redundancy in vision transformer for effi- cient image recognition. arXiv preprint arXiv:2402.00033, 2024.","title":"Lf-vit: Reducing spatial redundancy in vision transformer for efficient image recognition","authors":"Youbing Hu, Yun Cheng, Anqi Lu, Zhiqiang Cao, Dawei Wei, Jie Liu, Zhijun Li","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Khan et al., 2022] Salman Khan, Muzammal Naseer, Mu- nawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM Comput. Surv., 54(10s), sep 2022.","title":"Transformers in vision: A survey","authors":"Salman Khan, Muzammal Naseer, Munawar Hayat, Fahad Syed Waqas Zamir, Mubarak Shahbaz Khan, Shah","year":"2022","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lai et al., 2022] Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, and Jiaya Jia. Stratified transformer for 3d point cloud segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8500- 8509, June 2022.","title":"Stratified transformer for 3d point cloud segmentation","authors":"Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2023a] Wenwei Li, Ruofeng Liu, Shuai Wang, Dongjiang Cao, and Wenchao Jiang. Egocentric human pose estimation using head-mounted mmwave radar. In Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems, pages 431-444, 2023.","title":"Egocentric human pose estimation using head-mounted mmwave radar","authors":"Li","year":"2023","venue":"Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2023b] Yue Li, Jiayong Peng, Juntian Ye, Yueyi Zhang, Feihu Xu, and Zhiwei Xiong. Nlost: Non-line- of-sight imaging with transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13313-13322, June 2023.","title":"Nlost: Non-lineof-sight imaging with transformer","authors":"Li","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Martin et al., 2022] Daniel Martin, Sandra Malpica, Diego Gutierrez, Belen Masia, and Ana Serrano. Multimodality in vr: A survey. ACM Comput. Surv., 54(10s), sep 2022.","title":"Multimodality in vr: A survey","authors":"Daniel Martin, Sandra Malpica, Diego Gutierrez, Belen Masia, Ana Serrano","year":"2022","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Qiu et al., 2023] Yanlong Qiu, Jiaxi Zhang, Yanjiao Chen, Jin Zhang, and Bo Ji. Radar2: Passive spy radar detection and localization using cots mmwave radar. IEEE Trans- actions on Information Forensics and Security, 18:2810- 2825, 2023.","title":"Radar2: Passive spy radar detection and localization using cots mmwave radar","authors":"Qiu","year":"2023","venue":"IEEE Transactions on Information Forensics and Security","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Selva et al., 2023] Javier Selva, Anders S. Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B. Moeslund, and Albert Clapés. Video transformers: A survey. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 45(11):12922-12943, 2023.","title":"Video transformers: A survey","authors":"Javier Selva, Anders S Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B Moeslund, Albert Clapés","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Slocum et al., 2023] Carter Slocum, Yicheng Zhang, Nael Abu-Ghazaleh, and Jiasi Chen. Going through the mo- tions: AR/VR keylogging from user head motions. In 32nd USENIX Security Symposium (USENIX Security 23), pages 159-174, Anaheim, CA, August 2023. USENIX As- sociation.","title":"Going through the motions: AR/VR keylogging from user head motions","authors":"Carter Slocum, Yicheng Zhang, Nael Abu-Ghazaleh, Jiasi Chen","year":"2023","venue":"32nd USENIX Security Symposium (USENIX Security 23)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sun et al., 2023] Jiahao Sun, Chunmei Qing, Junpeng Tan, and Xiangmin Xu. Superpoint transformer for 3d scene in- stance segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(2):2393-2401, Jun. 2023.","title":"Superpoint transformer for 3d scene instance segmentation","authors":"Jiahao Sun, Chunmei Qing, Junpeng Tan, Xiangmin Xu","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2022] Jiayun Wang, Rudrasis Chakraborty, and Stella X. Yu. Transformer for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 44(8):4419-4431, 2022.","title":"Transformer for 3d point clouds","authors":"Jiayun Wang, Rudrasis Chakraborty, Stella X Yu","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2023a] Shuai Wang, Dongjiang Cao, Ruofeng Liu, Wenchao Jiang, Tianshun Yao, and Chris Xiaox- uan Lu. Human parsing with joint learning for dynamic mmwave radar point cloud. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technolo- gies, 7(1):1-22, 2023.","title":"Human parsing with joint learning for dynamic mmwave radar point cloud","authors":"Wang","year":"2023","venue":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2023b] Shuai Wang, Luoyu Mei, Zhimeng Yin, Hao Li, Ruofeng Liu, Wenchao Jiang, and Chris Xiaox- uan Lu. End-to-end target liveness detection via mmwave radar and vision fusion for autonomous vehicles. ACM Trans. Sen. Netw., oct 2023. Just Accepted.","title":"End-to-end target liveness detection via mmwave radar and vision fusion for autonomous vehicles","authors":"Wang","year":"2023","venue":"ACM Trans. Sen. Netw","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2023c] Yuntao Wang, Zhou Su, Ning Zhang, Rui Xing, Dongxiao Liu, Tom H. Luan, and Xuemin Shen. A survey on metaverse: Fundamentals, security, and privacy. IEEE Communications Surveys & Tutorials, 25(1):319-352, 2023.","title":"A survey on metaverse: Fundamentals, security, and privacy","authors":"Wang","year":"2023","venue":"IEEE Communications Surveys & Tutorials","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2024] Shuai Wang, Luoyu Mei, Ruofeng Liu, Wenchao Jiang, Zhimeng Yin, Xianjun Deng, and Tian He. Multi-modal fusion sensing: A comprehensive review of millimeter-wave radar and its integration with other modalities. IEEE Communications Surveys & Tutorials, pages 1-1, 2024.","title":"Multi-modal fusion sensing: A comprehensive review of millimeter-wave radar and its integration with other modalities","authors":"Shuai Wang, Luoyu Mei, Ruofeng Liu, Wenchao Jiang, Zhimeng Yin, Xianjun Deng, Tian He","year":"2024","venue":"IEEE Communications Surveys & Tutorials","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wu et al., 2022a] Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13587-13597, June 2022.","title":"Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition","authors":"Wu","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wu et al., 2022b] Xiaoyang Wu, Yixing Lao, Li Jiang, Xi- hui Liu, and Hengshuang Zhao. Point transformer v2: Grouped vector attention and partition-based pooling. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Pro- cessing Systems, volume 35, pages 33330-33342.","title":"Point transformer v2: Grouped vector attention and partition-based pooling","authors":"Wu","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"Curran Associates, Inc., 2022.","title":null,"authors":"Curran Associates, Inc","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wu et al., 2023] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. Point transformer v3: Simpler, faster, stronger. arXiv preprint arXiv:2312.10035, 2023.","title":"Point transformer v3: Simpler, faster, stronger","authors":"Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xie et al., 2021] Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu. Generative pointnet: Deep energy-based learning on unordered point sets for 3d generation, reconstruction and classification. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14976-14985, June 2021.","title":"Generative pointnet: Deep energy-based learning on unordered point sets for 3d generation, reconstruction and classification","authors":"Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xu et al., 2023] Peng Xu, Xiatian Zhu, and David A. Clifton. Multimodal learning with transformers: A sur- vey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):12113-12132, 2023.","title":"Multimodal learning with transformers: A survey","authors":"Peng Xu, Xiatian Zhu, David A Clifton","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yang et al., 2022] Jiewen Yang, Xingbo Dong, Liujun Liu, Chao Zhang, Jiajun Shen, and Dahai Yu. Recurring the transformer for video action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 14063-14073, June 2022.","title":"Recurring the transformer for video action recognition","authors":"Yang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yu et al., 2023] Hao Yu, Zheng Qin, Ji Hou, Mahdi Saleh, Dongsheng Li, Benjamin Busam, and Slobodan Ilic. Rotation-invariant transformer for point cloud matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5384- 5393, June 2023.","title":"Rotation-invariant transformer for point cloud matching","authors":"Hao Yu, Zheng Qin, Ji Hou, Mahdi Saleh, Dongsheng Li, Benjamin Busam, Slobodan Ilic","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2022] Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, and Yifan liu. Segvit: Semantic segmentation with plain vision trans- formers. In S. Koyejo, S. Mohamed, A. Agarwal, D. Bel- grave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 4971- 4982.","title":"Segvit: Semantic segmentation with plain vision transformers","authors":"Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, Yifan Liu","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"Curran Associates, Inc., 2022.","title":null,"authors":"Curran Associates, Inc","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2023a] Jia Zhang, Rui Xi, Yuan He, Yimiao Sun, Xiuzhen Guo, Weiguo Wang, Xin Na, Yunhao Liu, Zhenguo Shi, and Tao Gu. A survey of mmwave-based human sensing: Technology, platforms and applications. IEEE Communications Surveys & Tutorials, 25(4):2052- 2087, 2023.","title":"A survey of mmwave-based human sensing: Technology, platforms and applications","authors":"Zhang","year":"2023","venue":"IEEE Communications Surveys & Tutorials","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2023b] Lei Zhang, Ashutosh Agrawal, Steve Oney, and Anhong Guo. Vrgit: A version control sys- tem for collaborative content creation in virtual reality. In Proceedings of the 2023 CHI Conference on Human Fac- tors in Computing Systems, CHI '23, New York, NY, USA, 2023. Association for Computing Machinery.","title":"Vrgit: A version control system for collaborative content creation in virtual reality","authors":"Zhang","year":"2023","venue":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI '23","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2023c] Zhuoyang Zhang, Yuhao Dong, Yunze Liu, and Li Yi. Complete-to-partial 4d distillation for self- supervised point cloud sequence representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17661- 17670, June 2023.","title":"Complete-to-partial 4d distillation for selfsupervised point cloud sequence representation learning","authors":"Zhang","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhao et al., 2021] Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H.S. Torr, and Vladlen Koltun. Point transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 16259-16268, October 2021.","title":"Point transformer","authors":"Hengshuang Zhao, Li Jiang, Jiaya Jia, H S Philip, Vladlen Torr, Koltun","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}